Image modeling using higher-order statistics with application to predictive image coding

The mathematical framework to develop parametric image models based on higher-order statistics (HOS) is discussed. For non-Gaussian images, the model parameters will depend on the HOS if the model has a nonlinear form or if a fidelity criterion other than the mean square error (MSE) is utilized. Three new image models are proposed: (i) a nonlinear model based on the MSE criterion, (ii) a linear model under a criterion other than the MSE, and (iii) a nonlinear model using a criterion other than the MSE. These models are applied to predictive image coding, and the results are compared with those obtained by a linear model based on the MSE criterion.<
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